Branch-specific plasticity enables self-organization of nonlinear
computation in single neurons
R. Legenstein and W. Maass
It has been conjectured that nonlinear processing in dendritic branches endows
individual neurons with the capability to perform complex computational
operations that are needed in order to solve for example the binding problem.
However, it is not clear how single neurons could acquire such functionality
in a self-organized manner, since most theoretical studies of synaptic
plasticity and learning concentrate on neuron models without nonlinear
dendritic properties. In the meantime, a complex picture of information
processing with dendritic spikes and a variety of plasticity mechanisms in
single neurons has emerged from experiments. In particular, new experimental
data on dendritic branch strength potentiation in rat hippocampus have not
yet been incorporated into such models. In this article, we investigate how
experimentally observed plasticity mechanisms, such as
depolarization-dependent STDP and branch-strength potentiation could be
integrated to self-organize nonlinear neural computations with dendritic
spikes. We provide a mathematical proof that in a simplified setup these
plasticity mechanisms induce a competition between dendritic branches, a
novel concept in the analysis of single neuron adaptivity. We show via
computer simulations that such dendritic competition enables a single neuron
to become member of several neuronal ensembles, and to acquire nonlinear
computational capabilities, such as for example the capability to bind
multiple input features. Hence our results suggest that nonlinear neural
computation may self-organize in single neurons through the interaction of
local synaptic and dendritic plasticity mechanisms.
Reference: R. Legenstein and W. Maass.
Branch-specific plasticity enables self-organization of nonlinear computation
in single neurons.
The Journal of Neuroscience, 31(30):10787-10802, 2011.